Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By : Julio Cesar Rodriguez Martino
Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By: Julio Cesar Rodriguez Martino

Overview of this book

We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Machine Learning Basics
4
Section 2: Data Collection and Preparation
8
Section 3: Analytics and Machine Learning Models
11
Section 4: Data Visualization and Advanced Machine Learning

Who this book is for

This book is aimed at data analysts using Excel as their everyday tool, who need to go beyond Power Pivot and use add-ins and other advanced tools. Excel experts wanting to expand their knowledge to take advantage of the new connection possibilities between Excel and Azure will also benefit, as will project managers needing to test machine learning models without writing code.

It is generally taken for granted that, in order to do data science, from data cleansing to visualization and machine learning models, you need to be a Python or R programmer. This is not the case nowadays, and the general tendency seems to be heading toward code-free data science. The reader needs to learn that there are other options, avoiding code to take Excel to the next level and use it as a platform for professional data analysis and visualization.